Abstract

Background The nosological status of auditory hallucinations in
non-clinical samples is unclear.

Aims To investigate the functional neural basis of non-clinical
hallucinations.

Method After selection from 1206 people, 68 participants of high,
medium and low hallucination proneness completed a task designed to elicit
verbal hallucinatory phenomena under conditions of stimulus degradation. Eight
subjects who reported hearing a voice when none was present repeated the task
during functional imaging.

Results During the signal detection task, the high
hallucination-prone participants reported a voice to be present when it was
not (false alarms) significantly more often than the average or low
participants (P<0.03, d.f.=2). On functional magnetic resonance
imaging, patterns of activation during these false alarms showed activation in
the superior and middle temporal cortex (P<0.001).

Conclusions Auditory hallucinatory experiences reported in
non-clinical samples appear to be mediated by similar patterns of cerebral
activation as found during hallucinations in schizophrenia.

Studies using functional brain imaging in people with schizophrenia have
shown distributed patterns of activity during auditory hallucinations
(Shergill et al,
2000; McGuire et al,
2001). There are constraints to the approach of using clinical
samples, such as confounds of additional symptomatology and medication. Using
a proxy for hallucinations such as inner speech
(Shergill et al,
2002) can overcome some confounds but fails to capture the
autonomous quality of auditory hallucinations.

Non-clinical isolated psychotic symptoms such as auditory hallucinations
may represent part of an extended phenotype of psychosis
(Johns et al, 2004).
Predictors of isolated psychotic symptoms resemble those for schizophrenia,
such as drug use and urban upbringing (van
Os et al, 2000; Johns
et al, 2004). The current study aimed to show that
self-reported hallucinatory experiences are (a) accompanied by an information
processing bias in a signal detection task, and (b) associated with patterns
of cerebral activation similar to those reported for auditory hallucinations
in clinical samples.

METHOD

Participants

Stage 1 of the study aimed to identify normal participants in the main age
of risk for psychosis who were prone to auditory hallucinatory phenomena. One
of the most widely used measures of ‘psychosis-proneness’ is
schizotypy. A university intranet was used to administer online questionnaires
to a sample of students. The questionnaires were (a) the Oxford Liverpool
Inventory of Feelings and Experiences (O–LIFE,
Mason et al, 1995), a
well-validated self-report questionnaire to assess schizotypy, which comprises
four subscales (unusual experiences (UE), cognitive disorganisation (CD),
introvertive anhedonia (IA), impulsive nonconformity (IN)), and (b) the
Launay–Slade Hallucinations Scale (LSHS;
Launay & Slade, 1981).
These questionnaires were made known to students using electronic means (email
or a pop-up message). Participants gave their email address to allow the
researchers to contact them for later phases of the study (these were kept
separately from the data). Responses were securely stored, along with the
collection of basic demographic data. A total of 1206 people completed these
questionnaires. Participants were not given any incentive to complete the
intranet-based questionnaire. Ethical approval from the University of
Manchester ethics committee was given and participants gave informed
consent.

For the auditory signal detection task (stage 2), 63 participants
comprising three subgroups were chosen on the basis they scored highly, around
the mean or low on both ‘positive’ schizotypy score and reported
hallucinations. This was defined as +/–1 s.d. (or around the mean) on
both the unusual experiences sub-scale from the O–LIFE and on the LSHS,
since these were thought to be the most sensitive measures of the positive
aspects of schizotypy. This gave three subgroups of high, medium and low ‘
hallucination proneness’. For these subgroups, scores from the
online tests were confirmed by administering the same scales at interview. In
addition, participants completed the Schizotypal Personality Questionnaire
(SPQ; Raine, 1991). The SPQ
measures schizotypal traits using the diagnostic criteria for schizotypal
personality disorder from DSM–III–R, which offered the opportunity
to validate the dimensional measurement of positive schizotypy (the
O–LIFE) against a more clinical profile. Participants also completed a
semi-structured interview to assess substance use and the presence or history
of major depression or psychotic disorder
(Endicott & Spitzer,
1978).

For the functional imaging experiment (stage 3), 8 participants from the
high hallucination prone group, who completed the signal detection paradigm
outside the scanner and produced a high number of false alarm responses,
underwent a similar paradigm in a functional magnetic resonance imaging (fMRI)
scanner. Participants were selected on the basis that they reported a high
number of the false alarms outside the scanner and agreed to the scanning
study. These were 2 females and 6 males who were psychologically healthy and
right-handed. Participants used their preferred hand to respond using the
button box.

Auditory signal detection task

The signal detection task was developed to elicit hallucinatory phenomena
under ambiguous conditions. The stimulus train delivered over headphones
lasted 8 min and each participant completed it three times. It was devised in
a computerised audio editing programme. The 8-min period was divided into 8
s-epochs, which comprised 5 s of white noise (unpatterned hissing) and 3 s
marked by the absence of the white noise. The fMRI scanner noise was played
throughout the 8 min. During 60% of the 5 s-epochs of white noise a 1
s-snippet of androgynous voice was presented in the middle second. A third of
the time the voice snippets were clearly audible, while the others were
presented at auditory threshold. (Both volumes were previously determined
using a ‘hearing test’ completed under the same conditions with
different participants of a similar age. It would have been inappropriate to
set the threshold for each participant separately for two reasons: (a) it
would have sensitised the participants to the paradigm and altered their
threshold; and, (b) signal detection analysis assumes that every participant
hears the same stimuli, which would not have been the case with individual
thresholds.) Participants listened to the paradigm through standard stereo
headphones. Participants responded either ‘Yes’ or ‘
No’ by pressing one of two buttons with their preferred hand when
the white noise stopped to indicate whether they had perceived a voice. The
speed with which participants responded was also recorded.

Functional magnetic resonance image acquisition

A 1.5 T Phillips scanner, with a TR of 2.98 s and a TE of 40 ms was used.
In total, 38 brain slices were acquired and 160 brain volumes were taken;
slice thickness was 3.5mm. Transverse slice acquisition was used and slices
were acquired contiguously.

Each participant completed the signal detection paradigm three times while
in the scanner. The stimuli were delivered through plastic tubing leading to
headphones, and amplified appropriately. (A participant was placed in the
scanner and played the stimuli at varying volume levels in order to determine
the volume that made the clearly audible and threshold voices appropriately
difficult to detect. The results from this pilot work are not reported
here.)

The signal detection paradigm was similar to that completed outside the
scanner with two amendments. First, the white noise epochs were decreased to 3
s to increase the number of white noise epochs, increasing the opportunities
for participants to experience false alarms. Second, the periods marked by the
absence of white noise varied between 3 and 6 s to randomise slice
acquisition.

Statistical and image analysis

For the signal detection experiment, four pieces of information were
determined for each participant:

Hits: a positive response when a voice was present (either at the clearly
audible or threshold volume)

Correct rejections: a negative response when a voice was not present

Misses: a negative response when a voice was presented

False alarms: a positive response when a voice was not present (the
phenomena under study).

From the relationship between the hits and false alarms two measures can be
calculated: d′ (d prime) and B″ (or β). The d′ value is
a measure of sensitivity to the detection of the voice: a d′ of zero
indicates zero sensitivity to detecting a true signal, whereas increasingly
positive values indicate more sensitivity for detecting signals. The β
value indicates the degree to which each participant has a bias towards
responding ‘Yes’. It varies between –1 and +1 and values
below +1 indicate a lax criterion (i.e. being more likely to respond
positively in the absence of a true stimulus). The formula used to calculate
B″ was as follows:

For the fMRI experiment, an event-related approach was taken to the
analysis. The primary events of interest were the false alarms, since it was
thought these were indicative of non-clinical auditory hallucinations. The
analysis was performed in Statistical Parametric Mapping 2 (Wellcome Trust
Department of Cognitive Neurology, University College, London). The standard
pre-processing procedures of realignment, normalising and smoothing were
performed. Two subtractions were completed:

False alarms minus hits: to examine any areas activated by
hallucination-like phenomena in addition to areas activated by hearing a voice
which was present.

Contrast images were produced for each participant and placed into a random
effects analysis. A one-sample t-test was used. Z scores
above 3.09 were taken to be significant (approximating to a P=0.001
level of uncorrected significance). The voxel level approach and the
uncorrected significance levels were used since a descriptive approach was
taken to the data. Talairach & Tournoux’s
(1988) atlas was used.
Schmahmann et al
(1999) was used to determine
the structural localisation of the cerebellum activation.

RESULTS

On-line assessments

There were 1206 respondents (35% male, mean age of 22.5 years, (s.d.=6.2).
Three schizotypy sub-scales were normally distributed, all had means similar
to those previously reported: unusual experiences: 10.26 (6.59); cognitive
disorganisation: 12.92 (5.72); introvertive anhedonia (not normally
distributed): 13.87 (2.72); impulsive nonconformity: 11.85 (3.17). Some
respondents positively endorsed hallucination-like phenomena items from the
LSHS (e.g. 19.5% had heard a voice speaking their thoughts aloud; 10.4% had
been troubled by hearing voices in their head). The O–LIFE total score
correlated with the LSHS total (Pearson’s r=0.68,
P<0.0001).

Participants completed the O–LIFE again when interviewed. The LSHS
and O–LIFE unusual experiences sub-scale both showed good
test–retest reliability, with Pearson’s correlations of 0.76
(P<0.001) and 0.65 (P>0.001) respectively.
Participants also completed the SPQ, which has three dimensions: cognitive
perceptual, disorganised and interpersonal. Pearson’s correlations
between the SPQ dimensions, the O–LIFE sub-scales and the LSHS varied
between 0.21 and 0.70 (all P<0.05). The introvertive anhedonia
sub-scale did not correlate with any SPQ dimensions.

The signal detection task was completed by 63 participants (49% were male,
mean age 23.9 years (s.d.=8.6)). The participants in the high schizotypy
(‘high hallucination prone’; n=30), mean (‘medium
hallucination prone’; n=15), and low schizotypy
(‘low/non-hallucination score’, n=18) groups (see
Fig. 1) did not differ on
gender, age, university attended (χ2=1.36, d.f.=2, NS), or
handedness (χ2=2.81, d.f.=2, NS). The majority of the sample
(72%) reported smoking cannabis at least once in their lifetime. Other
recreational drugs reported were (listed according to frequency, with a
reporting rate of at least 10%): cocaine (19%), ecstasy (19%), lysergic acid
diethylamide (13%) and amphetamine (11%).

Signal detection task

Sensitivity

The d′ parameter is a measure of sensitivity of the
participant’s ability to detect the signals presented. The results for
the mean and low groups were similar to each other; consequently they were
merged to produce a control group (n=33) to compare with the high
hallucination prone group (n=30). Using independent t-tests
there were no group differences on d′ for the 3 trials. For the high
hallucination prone group the means (s.d.s) were: trial 1: 1.64 (0.60), trial
2: 2.10 (0.81), trial 3: 2.10 (0.78). For the rest of the sample the means
(s.d.s) were: trial 1: 1.86 (0.76), trial 2: 2.15 (0.63), trial 3: 2.25
(0.60).

Decision-making bias

The measure of bias indicates the tendency to respond positively, even in
the absence of external stimulus (false alarm). The data for the three groups
of participants (3 levels of between subjects) over the three repeated trials
(3-levels of within subjects) were entered into a repeated measures analysis
of covariance (ANCOVA) (see Fig.
2). There was no effect of trial repetition (F=0.056,
d.f.=1, NS), nor was there an interaction between group and trial repetition
(F=0.702, d.f.=2, NS). There was a significant main effect of
hallucination proneness group (F=3.84, d.f.=2, 60, P=0.027)
with the high group scoring significantly lower than the low group in
preplanned least squares difference analysis (mean difference –0.37,
P=0.01). This indicates the high hallucination prone group responded
positively more frequently regardless of whether a voice was present, i.e.
they reported more false alarms.

Means and standard deviations of the β value for the three groups of
participants over the three repeated trials of the signal detection paradigm. ░
, B″1; ▪, B″2; □, B″3. The β value
measures the bias to respond positively in the absence of a true stimulus,
with a score of 1.0 meaning a bias not to respond (conservative), and a score
of 0.0 a bias to respond (liberal).

Effects of drug use on false alarms

The high hallucination prone group were more likely to have smoked cannabis
than the low or mean groups (χ=7.10, d.f.=2, P=0.029). The effect
of hallucination proneness group and cannabis use on total false alarms was
investigated using a one-way ANOVA. There was a significant main effect of
group (F(2,53)=3.07, P=0.03) but there was no significant
main effect of having smoked cannabis (F(1,53)=0.50, NS) nor a
significant interaction between having smoked cannabis and hallucination
proneness group (F(2,53)=1.08, NS) on the rates of false alarms.

Reaction time

The speed of responding was recorded. There were complete data for 30 high
hallucination prone, 14 mean scoring and 15 low hallucination prone
participants. Response times from the three repetitions were placed in a
repeated measures ANOVA with schizotypy group as an independent variable.
There were no significant main effects for trial repetition (F=2.95,
d.f.=1, NS) or proneness group (F=2.70, d.f.=2, NS). However, there
was a significant interaction between response time and participant group
(F=4.03, d.f.=2, P=0.02). High hallucination prone
participants accelerated their response times across the three trials, unlike
participants in the other groups. For the high group mean reaction times
(s.d.) were: repeat 1: 0.14 (0.07), repeat 2: 0.13 (0.07), repeat 3: 0.11
(0.06). For the mean group mean reaction times were: repeat 1: 0.12 (0.05),
repeat 2: 0.12 (0.06), repeat 3: 0.12 (0.06). Mean reaction times for the
non-prone group were: repeat 1: 0.08 (0.04), repeat 2: 0.10 (0.08), repeat 3:
0.09 (0.03).

Functional magnetic resonance imaging

(a) False alarms minus correct rejections

This subtraction revealed brain areas of activation associated with false
alarms, with the influence of white noise removed. It was intended to reveal
the brain areas activated by hallucination-like phenomena. The results in
Table 1 (see also Fig. DS1 in
the data supplement of the online version of this paper) show that there were
four discrete areas of activation: the right middle temporal gyrus, bilateral
fusiform gyrus and the right putamen.

(b) False alarms minus hits

This subtraction revealed areas activated during false alarms which were in
addition to those activated when a voice was present. The results in
Table 1 (see also Fig. DS2 in
the data supplement of the online version of this paper) show that two
clusters in the right superior frontal gyrus, the right middle frontal gyrus,
the left cingulate gyrus, bilateral superior temporal gyrus the left middle
temporal and the left cerebellum.

DISCUSSION

The aim of this study was to characterise and validate the non-clinical
hallucination-like experiences reported in community surveys. We used the
intranet to recruit a large sample of normal participants. Subgroups were
identified on the basis of self-reported hallucinatory experiences and
positive schizotypy score. Scores were validated at interview. We hypothesised
that people reporting such experiences would show a response bias in a signal
detection experiment. The reasons behind using the signal detection task were
both practical and theoretically based. The paradigm has previously been used
to examine proneness to hallucinations (e.g.
Bentall & Slade, 1985) with
some success. The signal detection task allowed proneness to false perceptions
to be measured objectively. Additionally, since the stimuli in the task were
required to be ambiguous (to increase the difficulty) the scanning environment
did not compromise the validity of the results.

The results showed that ‘highly hallucination-prone’
participants reported more false perceptions of voices in conditions of
stimulus ambiguity in the signal detection task. The data showed this to arise
from a decisional bias, and not simply a decreased sensitivity, when compared
to non-hallucination prone participants, replicating the results of Bentall & Slade (1985) and Rankin & O’Carroll (1995).
Ishigaki & Tanner (1999)
agreed that decisional bias distinguished patients with schizophrenia from
healthy controls, although they found a co-existing low sensitivity to
detecting signals. Importantly, the data showed that distribution of the false
alarms in the sample was highly positively skewed, indicating that only a
small number of the high schizotypes reported a large number of false alarms.
This suggests that it requires more than high schizotypy scores for
participants to be prone to false perceptions. From the current study it was
not possible to determine what the additional factors may be.

A finding which was not part of the original hypothesis was that the
reaction (or decision-making) time in the high hallucination prone group
became faster over repetitions of the experiment when compared to the other
two groups, suggesting that these subjects became increasingly confident in
the interpretation of their perceptual experiences, correct or not. This
increase in reaction time in the high hallucination prone group suggests a ‘
jumping-to-conclusions’ style of thinking associated with
delusional ideation (Garety et al,
1991).

In the fMRI study a control group of either low or mean scoring schizotypes
were not included in the imaging tasks since these groups do not produce false
alarms. The distribution of the false alarms was highly positively skewed and
only a small number of the high schizotypes reported a large number of false
alarms. This in itself is of interest since it suggests that it requires more
than high schizotypy scores for participants to be prone to false perceptions.
However, from the current study it was not possible to determine what the
additional factors may be. Participants were asked to respond only when they
heard a voice, and the areas which were activated (see below) seem to suggest
it is speech that was being processed during the false alarms.

Functional magnetic resonance imaging was used to reveal areas of the brain
mediated during the false alarm events, when subjects were erroneously hearing
a voice. The false alarms versus correct rejections subtraction aimed to
reveal areas activated during auditory hallucinations with background white
noise, compared to white noise alone. Participants were asked to respond only
when they heard a voice and the areas which were activated seem to suggest it
is speech which was being processed during the false alarms. The subtraction
revealed activations in the right middle temporal gyrus, the right and left
fusiform and the putamen. In previous studies, the middle temporal gyrus has
been reported to be associated with auditory hallucinations in clinical
samples (Lennox et al,
1999) when compared to rest. There is some suggestion the fusiform
may be involved in the production of mental imagery
(Wise et al, 2000).
The putamen has a preferential activation for speech and its content
(Friederici et al,
2003).

The false alarms versus hits subtraction highlighted areas activated during
auditory hallucinations compared to detecting real speech. This subtraction
would remove any areas activated by attending to speech since in both
situations speech is being processed. It revealed activations in the right
superior frontal gyrus, the left and right middle frontal gyrus, the left
cingulate gyrus, the left and right superior temporal gyrus and the left
cerebellum. Previous studies have shown that the middle frontal gyrus has
preferential activation for natural speech
(Benson et al, 2001).
The cingulate has been reported to be involved in a network for the
recognition and processes of language. The superior temporal gyrus has been
implicated in previous studies comparing auditory hallucinations to rest
(Lennox et al, 1999)
during varying rates of inner speech in controls
(Shergill et al,
2002). The cerebellum has been reported in a recent study in
subtractions comparing certain to uncertain decision making
(Blackwood et al,
2004). The authors suggest that the cerebellum is involved in
making probabilistic decisions under ambiguous circumstances. This explanation
appears applicable to the current paradigm.

Taken together, the behavioural and imaging results suggest our signal
detection task confirms that a cognitive bias underlies proneness to
non-clinical auditory hallucinations and that these experiences are mediated
by similar areas of activation to those found in auditory hallucinations and
inner speech in patients with schizophrenia. This is particularly the case for
the temporal lobe and fusiform activations. The task which elicited the
cerebellum activation in the Blackwood et al
(2004) study was the Beads
task, used to examine a ‘jumping-to-conclusions’ style of thinking
which may underpin delusion formation
(Garety et al, 1991).
One model of how unusual perceptual experiences progress to full auditory
hallucinations and secondary delusions is via a cognitive style involving an
over-readiness to form judgements from ambiguous information (‘jumping
to conclusions’) (Garety et
al, 2001), elicited by the Beads task. The cerebellar
activation in the current study may reflect this mechanism, shown
behaviourally in the decreasing reaction time over trials, which was confined
to the hallucination-prone group.

The study design has limitations. First, although web-based ascertainment
of participants is efficient, the resulting sample is not likely to be
representative of the population at large. However, this was not necessary to
test the main hypotheses of the study which involved within-group comparisons.
Second, the role of alcohol and street drug use may be important. We collected
data on this, particularly cannabis use, which are reported elsewhere
(Barkus et al, 2006).
Third, it remains open to question whether the phenomenology of the
hallucinatory experiences elicited in this study are a truly valid model of
those found in psychotic disorders. The experiences were the false perceptions
of single words during artificial conditions of ambiguity, in the context of
manufactured expectation. Although we suggest that this reflects a cognitive
bias important in all hallucinatory experience, it is clearly not
phenomenologically identical to the spontaneous experience of complex voice
hearing which characterises schizophrenia. Direct comparison with patients
with schizophrenia during a signal detection task may clarify these
issues.

Acknowledgments

This project was funded by the Stanley Medical Research Institute and the
Mason Medical Research Foundation.